Image processing device generating binary image data by selecting specific component

ABSTRACT

An image processing device includes a processor configured to perform: acquiring target image data representing a target image; and generating binary image data representing the letter in the target image by using the target image data. The generating of the binary image data comprises: identifying a background color value representing color of background of the target image; identifying a letter color value representing color of the letter in the target image; acquiring a difference between the background color value and the letter color value, the difference including a plurality of component differences; selecting one specific component image data corresponding to a specific component from among the plurality of components, the specific component corresponding to a maximum component difference among the plurality of component differences; and performing a binarizing process on the selected one specific component image data to generate one binary image data.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority from Japanese Patent Application No.2012-081492 filed Mar. 30, 2012. The entire content of the priorityapplication is incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to an image process for processing imagedata representing images that include text.

BACKGROUND

There has been a need for a technology to generate binary image datathat can suitably render text from target image data representing atarget image that includes text. This binary image data is useful forcompressing the target image data and for performing characterrecognition processes (OCR). One such technique known in the artconverts RGB image data to binary values for each color component andproduces binary image data by acquiring the logical sum (inclusive OR)of the binary data for the three color components.

SUMMARY

However, the conventional technology does not give sufficientconsideration to setting threshold values used in the thresholdingprocess. If suitable threshold values are not set, then suitable binaryimage data for rendering text cannot be generated.

Therefore, the primary object of the present invention is to provide anew technology capable of producing suitable binary image data forrendering text from image data representing an image that includes text.

In order to attain the above and other objects, the invention providesan image processing device comprising: a processor to perform: aprocessor configured to perform: acquiring target image datarepresenting a target image including a letter, the target image dataincluding a plurality of component image data corresponding respectivelyto a plurality of components constituting a color coordinate system; andgenerating binary image data representing the letter in the target imageby using the target image data, the generating of the binary image datacomprising: identifying a background color value representing color ofbackground of the target image, the background color value beingcomposed of values for the plurality of components; identifying a lettercolor value representing color of the letter in the target image, theletter color value being composed of values for the plurality ofcomponents; acquiring a difference between the background color valueand the letter color value, the difference including a plurality ofcomponent differences corresponding respectively to the plurality ofcomponents; selecting, from among the plurality of component image data,one specific component image data corresponding to a specific componentfrom among the plurality of components, the specific componentcorresponding to a maximum component difference among the plurality ofcomponent differences; and performing a binarizing process on theselected one specific component image data to generate one binary imagedata.

According to another aspect, the present invention provides acomputer-readable storage medium storing computer-readable instructionsthat, when executed by a processor, causes an image processing device toperform: acquiring target image data representing a target imageincluding a letter, the target image data including a plurality ofcomponent image data corresponding respectively to a plurality ofcomponents constituting a color coordinate system; and generating binaryimage data representing the letter in the target image by using thetarget image data, the generating of the binary image data comprising:identifying a background color value representing color of background ofthe target image, the background color value being composed of valuesfor the plurality of components; identifying a letter color valuerepresenting color of the letter in the target image, the letter colorvalue being composed of values for the plurality of components;acquiring a difference between the background color value and the lettercolor value, the difference including a plurality of componentdifferences corresponding respectively to the plurality of components;selecting, from among the plurality of component image data, onespecific component image data corresponding to a specific component fromamong the plurality of components, the specific component correspondingto a maximum component difference among the plurality of componentdifferences; and performing a binarizing process on the selected onespecific component image data to generate one binary image data.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 is a block diagram showing a structure of a computer according toa first embodiment;

FIG. 2 is a flowchart illustrating steps in an image process executed bya scanner driver;

FIG. 3 is an explanatory diagram showing an example of a scanned imagerepresented by scan data;

FIG. 4 is an explanatory diagram showing an example of an edge imagerepresented by edge image data;

FIG. 5 is an explanatory diagram showing a sample of binary image datacorresponding to the scanned image in FIG. 3;

FIG. 6 is a flowchart illustrating steps in a labeling process;

FIG. 7 is an explanatory diagram illustrating the labeling process;

FIG. 8 is an explanatory diagram showing an example of a histogramrepresenting a brightness distribution of an object image;

FIG. 9 is an example of a determination table;

FIG. 10 is a flowchart illustrating steps in a compressed image datageneration process;

FIG. 11(A) is an explanatory diagram showing background imagecorresponding to the scanned image in FIG. 3;

FIG. 11(B) is an explanatory diagram showing three text imagesrepresented by text binary data obtained by thresholding three objectimages in the scanned image;

FIG. 12 is a flowchart illustrating steps in a text binary datageneration process in S550 of FIG. 2;

FIG. 13 is an explanatory diagram showing a text object image as anexample;

FIG. 14 is a flowchart illustrating steps in a text color identificationprocess;

FIG. 15 is an explanatory diagram showing an example of a histogram forthe text object image;

FIGS. 16(A)-(C) are explanatory diagrams illustrating a method ofselecting a representative component;

FIG. 17 is a flowchart illustrating steps in a threshold value settingprocess;

FIG. 18(A) is an explanatory diagram showing a sample text imagerendered by the text binary data generated in the text binary datageneration process according to the embodiment shown in FIG. 12;

FIG. 18(B) is an explanatory diagram showing a first comparative exampleof a text image rendered by binary data generated when the thresholdvalue is set too close to a background color value;

FIG. 18(C) is an explanatory diagram showing a second comparativeexample of a text image rendered by binary data generated when thethreshold value is set too far from the background color value;

FIG. 19(A) is a histogram of component values for a prescribed componentwhen a text sharpness is relatively low; and

FIG. 19(B) is a histogram of component values for a prescribed componentwhen the text sharpness is relatively high.

DETAILED DESCRIPTION A. First Embodiment A-1. Structure of an ImageProcessor

Next, a first embodiment of the present invention will be describedwhile referring to the accompanying drawings. FIG. 1 is a block diagramshowing the structure of a computer 200 serving as an embodiment of theimage processing device according to the present invention.

The computer 200 is a personal computer that includes a CPU 210; aninternal storage device 240 having ROM and RAM; an operating unit 270,such as a mouse and keyboard; a communication unit 280 that enables thecomputer 200 to communicate with external devices; and an externalstorage device 290, such as a hard disk drive.

The computer 200 is connected to and capable of communicating with ascanner 300 and a multifunction peripheral 400, both external devices,via the communication unit 280. The scanner 300 is an image-readingdevice that acquires scan data by optically reading an original. Themultifunction peripheral 400 includes an image-reading unit foracquiring scan data by optically reading an original.

The internal storage device 240 is provided with a buffer region 241 fortemporarily storing various intermediate data generated when the CPU 210performs processes. The external storage device 290 stores a driverprogram 291, and a determination table 292 referenced in an imageprocess described later. The driver program 291 is supplied on a CD-ROMor the like.

By executing the driver program 291, the CPU 210 functions as a scannerdriver 100. The scanner driver 100 executes an image process describedlater on scan data to generate a high-compression PDF file. The scannerdriver 100 includes a scan data acquisition portion 110, a regionidentification portion 120, and a text binary image generation portion130.

As will be described later, the region identification portion 120identifies a partial image corresponding to a region that includes text(a text object image) from scan data in order to acquire partial imagedata representing the text object image.

The text binary image generation portion 130 includes a temporaryidentification portion 131, a background color identification portion132, a text color identification portion 133, a difference acquisitionportion 134, a selection portion 135, a characteristic value settingportion 136, a threshold value setting portion 137, and a thresholdingprocess portion 138. The text binary image generation portion 130generates text binary data using the partial image data acquired by theregion identification portion 120.

A-2. Image Process

FIG. 2 is a flowchart illustrating steps in an image process executed bythe scanner driver 100. In S100 of the image process, the scan dataacquisition portion 110 of the scanner driver 100 acquires scan data tobe processed. More specifically, the scan data acquisition portion 110controls the scanner 300 or the image reading unit of the multifunctionperipheral 400 to generate scan data and acquires this scan data. Thescan data is bitmap data configured of RGB pixel data. RGB pixel data isimage data that includes three component values for each pixelcorresponding to the colors red (R), green (G), and blue (B) (componentvalues in the embodiment are 256-level gradation values).

FIG. 3 shows an example of a scanned image SI represented by the scandata. The scanned image SI includes a background image Bg1, a textobject Ob2 representing text, a photo object Ob3 representing aphotograph, and a drawing object Ob4 representing a drawing. A drawingobject in the embodiment includes objects representing an illustration,table, diagram, pattern, and the like. The text object Ob2 includesthree lines worth of text. Each line in the three-line set of text isexpressed in a different color.

In S150 the region identification portion 120 creates edge image datarepresenting an edge image EGI based on the scan data. Edge image datais obtained by applying the Sobel filter to each component value for theplurality of RGB pixels included in the scan data. The edge image datais configured of RGB pixel data for a plurality of pixels in the scandata. The RGB pixel data expresses the edge strength of thecorresponding pixel in the target image for each of the R, G, and Bcomponent values. More specifically, a prescribed component value S(x,y) for a pixel in the edge image at pixel position (x, y) is calculatedaccording to Equation (1) below using component values P of nine pixelsin the scan image SI.

                                     Equation  (1)${S( {x,y} )} = {{{\begin{bmatrix}{- 1} & 0 & 1 \\{- 2} & 0 & 2 \\{- 1} & 0 & 1\end{bmatrix}\begin{bmatrix}{P( {{x - 1},{y - 1}} )} & {P( {x,{y - 1}} )} & {P( {{x + 1},{y - 1}} )} \\{P( {{x - 1},y} )} & {P( {x,y} )} & {P( {{x + 1},y} )} \\{P( {{x - 1},{y + 1}} )} & {P( {x,{y + 1}} )} & {P( {{x + 1},{y + 1}} )}\end{bmatrix}}} + {{\begin{bmatrix}{- 1} & {- 2} & {- 1} \\0 & 0 & 0 \\1 & 2 & 1\end{bmatrix}\begin{bmatrix}{P( {{x - 1},{y - 1}} )} & {P( {x,{y - 1}} )} & {P( {{x + 1},{y - 1}} )} \\{P( {{x - 1},y} )} & {P( {x,y} )} & {P( {{x + 1},y} )} \\{P( {{x - 1},{y + 1}} )} & {P( {x,{y + 1}} )} & {P( {{x + 1},{y + 1}} )}\end{bmatrix}}}}$

As shown above in Equation (1), the nine pixels are positioned on allsides of the target pixel corresponding to pixel position (x, y) in theedge image EGI. The first and second terms on the right side of Equation(1) are absolute values for the sum of values obtained by multiplyingpixel values at the nine positions with corresponding coefficients. Ascan be seen from Equation (1), pixel data in the edge image EGI The edgeimage may be created using any of various edge detection filters, suchas the Prewitt filter and Roberts filter, and is not limited to theSobel filter. Edge strength represents the magnitude of change incomponent values between a target pixel and its peripheral pixels (wherethe magnitude of change is a differential value). The first term inEquation (1) is used to calculate differential in the horizontaldirection, while the second term is used to calculate differential inthe vertical direction.

FIG. 4 shows an example of the edge image EGI represented by edge imagedata. For convenience sake, pixels in FIG. 4 having low edge strengthare depicted in white, and pixels having high edge strength are depictedin black. The edge image EGI in this example includes edges Eg2-Eg4 forthe corresponding objects Ob2-Ob4 in the scanned image SI.

In S200 of FIG. 2, the region identification portion 120 configures aplurality of blocks B (see FIG. 4) for the edge image EGI. The blocks Bare arranged as a grid in relation to the edge image EGI. A single blockB is equivalent to the size of N×N pixels (where N is a prescribednatural number), for example. N is set to a value between 10 and 50.Since the edge image EGI and the scanned image SI are identical in size(have the same number of pixels, both vertically and horizontally), itcan also be said that the blocks B are configured for the scanned imageSI.

In S250 the region identification portion 120 identifies solid regionsand non-solid regions in units of blocks. Specifically, the regionidentification portion 120 calculates average edge strengths ERave,EGave, and EBave for each block B in the scanned image SI. The averageedge strengths ERave, EGave, and EBave are average values of thecorresponding component values R, G, and B averaged for all pixels inthe block B of the edge image EGI. The region identification portion 120compares the average edge strengths of the block B to prescribedreference values to classify the block B as either a solid block or anon-solid block. A solid block has smaller average edge strengths thanthe corresponding reference values, while a non-solid block has averageedge strengths greater than or equal to the reference values. Forexample, the region identification portion 120 compares the average edgestrengths ERave, EGave, and EBave to reference values ETr, ETg, and ETbset for corresponding color components. If ERave<ETr, EGave<ETg, andEBave<ETb, the region identification portion 120 classifies the targetblock B as a solid block. Conversely, if even one of the expressionsERave≧ETr, EGave≧ETg, and EBave≧ETb is satisfied, then the regionidentification portion 120 classifies the target block B as a non-solidblock.

Next, the region identification portion 120 identifies non-solid regionsconfigured of one or more non-solid blocks, and solid regions configuredof one or more solid blocks. That is, the region identification portion120 consolidates contiguous non-solid blocks into a single regionidentified as a non-solid region. Additionally, the regionidentification portion 120 identifies a single non-solid blocksurrounded entirely by solid blocks as a single non-solid region.Similarly, the region identification portion 120 consolidates contiguoussolid blocks into a single region identified as a solid region. Theregion identification portion 120 also identifies a single solid blocksurrounded entirely by non-solid blocks as a single solid region.

The scanned image SI of FIG. 3 has regions A1, A2, A3, and A4. As aresult of the process in S250, the region identification portion 120identifies blocks B within the regions A2, A3, and A4 as non-solidblocks and, hence identifies the regions A2, A3, and A4 as non-solidregions. Additionally, the region identification portion 120 identifiesthe A1, excluding the regions A2, A3, and A4, as a solid region. FromFIG. 3 it is clear that the non-solid regions A2, A3, and A4 correspondto the objects Ob2, Ob3, and Ob4, respectively. Similarly, the solidregion A1 corresponds to the background image Bg1. Since contiguousnon-solid blocks are identified collectively as a single non-solidregion, as described above, non-solid regions are normally surrounded bysolid regions.

In S300 the region identification portion 120 identifies a referencevalue for each color component in the scanned image SI needed to convertnon-solid regions to binary values by using each of the RGB colorcomponents of pixels in the solid regions surrounding the non-solidregion. Since each of the non-solid regions A2-A4 are surrounded by asingle solid region A1 in the example of FIG. 3, component values ofpixels in the solid region A1 are used to identify reference values forthresholding the non-solid regions A2-A4. More specifically, the regionidentification portion 120 calculates the average value of eachcomponent in the solid region A1 (background color average values BCR,BCG, and BCB) as the thresholding reference values. That is, the regionidentification portion 120 calculates the average value of each colorcomponent (average component value) for all pixels constituting thesolid region A1. Here, the mode, median, or other value related to eachcomponent may be employed as the thresholding reference value instead ofthe average value. The thresholding reference values may also beidentified from pixel data in a portion of the solid region that ispositioned near the non-solid regions.

In S350 the region identification portion 120 generates binary imagedata for each non-solid region based on the threshold reference valuesBCR, BCG, and BCB. That is, the region identification portion 120converts the value of each pixel constituting a non-solid region of thescanned image SI into binary values “0” and “1” and sorts the pixelsconstituting the non-solid region into object pixels corresponding to“1” and non-object pixels corresponding to “0”. The regionidentification portion 120 sets the pixel value for binary image datacorresponding to a pixel i to “0” when the component values Ri, Gi, andBi of the pixel i constituting a non-solid region satisfy all ofEquations (2)-(4) below and sets the pixel value to “1” when even one ofthe Equations (2)-(4) is not satisfied.

BCR−ΔV<Ri<BCR+ΔV  Equation (2)

BCG−ΔV<Gi<BCG+ΔV  Equation (3)

BCB−ΔV<Bi<BCB+ΔV  Equation (4)

Pixels in text binary data having a value of “1” are text pixelsconstituting text, while pixels having a value of “0” are non-textpixels that do not constitute text. As is clear from Equations (2)-(4),a pixel i is classified as a text pixel when the color of the pixel i issubstantially the same as the color of the solid region surrounding thepixel i (when the difference in color values is less than ΔV). The pixeli is classified as a non-text pixel when the color of the pixel idiffers from the color of the solid region around the pixel i (when thedifference in color values is greater than or equal to ΔV).

FIG. 5 shows a sample of binary image data corresponding to the scannedimage SI in FIG. 3. Dark portions in FIG. 5 represent images configuredof object pixels, while white portions represent images configured ofnon-object pixels.

In S400 of FIG. 2, the region identification portion 120 executes alabeling process. FIG. 6 is a flowchart illustrating steps in thislabeling process, and FIG. 7 is an explanatory diagram illustrating thelabeling process. FIG. 7 shows a partial binary image PWI correspondingto the non-solid region A2 in FIG. 3.

In S410 of the labeling process, the region identification portion 120labels object pixels and non-object pixels based on the binary imagedata (see FIG. 5). The region identification portion 120 assigns asingle label (identifier) to each set of object pixels configured ofcontiguous object pixels. In the example of FIG. 7, a separate label isassigned to each character, such as character BK11 and character BK12.When a single character is configured of separate parts, separate labelsare assigned to each part, as in the example of parts BK21 and BK22. Inaddition, a single label is assigned to each set of non-object pixelsconfigured of contiguous non-object pixels. In the example of FIG. 7,the peripheral part corresponding to the region surrounding the text isassigned the label WO1, while small regions enclosed within individualcharacters are assigned separate labels, as in the regions WO2 and WO3.

In S420 the region identification portion 120 consolidates (assigns thesame label to) sets of object pixels that meet a consolidatingcondition. The consolidating condition includes satisfying both ofcondition 1, which states that the distance between sets of pixels isless than a reference value, and condition 2, which states that thedifference in color between sets of pixels is less than a referencevalue. The distance between two object pixel sets may be represented bythe vertical distance and horizontal distance between minimum boundingrectangles of the object pixel sets. The color of an object pixel setmay be expressed by the average value of all pixel data in the objectpixel set of the scanned image SI for each color component (Rob, Gob,Bob). The color difference between any two object pixel sets isexpressed by the Euclidian distance between the two colors in the RGBcolor space.

When suitable reference values are set for the consolidating condition,the region identification portion 120 can consolidate a plurality ofobject pixel sets having substantially the same color and beingpositioned relatively close to one another and identifies the sets as asingle consolidated object pixel set. In addition, a plurality ofcharacters having essentially different colors can be identified asseparate object pixel sets, regardless of their distance from oneanother. As described above, the text object Ob2 in the scanned image SI(see FIG. 3) has a plurality of characters arranged in three rows, witheach row having a different color. Accordingly, in the example of FIG.7, the object pixels representing the text object Ob2 are consolidatedinto three consolidated object pixel sets representing the top row ofcharacters (alphabetic characters), the middle row of characters(symbols), and the bottom row of characters (alphabetic characters).

In S430 the region identification portion 120 consolidates (assigns thesame label to) a plurality of non-object pixel sets that meet aconsolidating condition. Specifically, the region identification portion120 consolidates a first non-object pixel set of a size smaller than areference value with a second non-object pixel set that is separatedfrom and surrounding the first non-object pixel set by object pixels.When suitable reference values are set for the consolidating condition,the region identification portion 120 can identify non-object pixel setsconstituting the background of groups of characters as a single pixelset. In the example of FIG. 7, the small regions WO2 and WO3 areconsolidated into a single non-object pixel set with the peripheralregion WO1.

After performing the labeling process described above, the regionidentification portion 120 has identified object images BK1-BK5indicated in FIG. 5 with dashed lines. Each object image is a partialimage corresponding to the minimum bounding rectangle for a set ofobject pixels assigned the same label. In the example of FIG. 5, theregion identification portion 120 has identified the five object imagesBK1-BK5 that correspond to the text object Ob2, photo object Ob3, anddrawing object Ob4 shown in FIG. 3. Hence, by performing the labelingprocess on the binary image data, the region identification portion 120identifies the five object images BK1-BK5 as partial images of thescanned image SI.

After completing the labeling process of S400, in S450 of FIG. 2 theregion identification portion 120 executes an object attributedetermination process to determine an attribute of each object image inthe scanned image SI. The region identification portion 120 determinesattributes of object pixels based on a pixel density D, distributionwidth W, and color number C for the object image being processed.

The pixel density D indicates the percentage of object pixels (pixelswhose value is “1” in the corresponding binary image data) that occupythe object image in the scanned image SI and is expressed by theequation D=No/Na. Here, “No” indicates the number of object pixels, and“Na” indicates the total number of pixels in the object image.

In S450 the region identification portion 120 calculates a brightnessdistribution for each object image in the scanned image SI. FIG. 8 showsan example of a histogram representing the brightness distribution of anobject image. The histogram in FIG. 8 is produced by plotting pixelcounts on the vertical axis for each of the 256 brightness values Y onthe horizontal axis. The region identification portion 120 calculatesthe brightness value Y using RGB pixel data (R, G, and B) in anequation, such as brightness valueY=((0.298912×R)+(0.586611×G)+(0.114478×B)).

The distribution width W may be, for example, the difference between theminimum value and maximum value of valid brightness values. The validbrightness values are brightness values Y for which the pixel countexceeds a reference number Th2 (brightness values corresponding to theshaded regions in FIG. 8) from among the 256 levels of brightness valuesY. In the example of FIG. 8, the distribution width W is the differencebetween the brightness value Y6 and the brightness value Y1.

The color number C in the embodiment is the number of valid brightnessvalues described above. Since the colors of these pixels differ fordifferent brightness values Y, the number of different brightness valuesY (the number of types of brightness values Y) represents the number ofcolors (the number of types of colors). In the example of FIG. 8, thecolor number C is equivalent to C1+C2+C3 (C1=Y2−Y1, C2=Y4−Y3, C3=Y6−Y5).

The region identification portion 120 identifies the attribute of theobject image based on the pixel density D, the distribution width W, andthe color number C described above. For example, the regionidentification portion 120 determines whether each of the pixel densityD, the distribution width W, and the color number C is greater than orequal to the corresponding reference value Dth, Wth, and Cth. Usingthese determination results, the region identification portion 120references the determination table 292 to determine the attribute of theobject image. In the embodiment, the region identification portion 120determines the attribute of each object image to be one of “photo,”“text,” and “drawing.”

As is clear from the determination table 292 in FIG. 9, the attribute ofa target region is determined to be “text” in the following two cases.

(1-1) Color number C<Cth and pixel density D<Dth(1-2) Distribution width W<Wth and color number C≧Cth and pixel densityD<Dth

The attribute of the target region is determined to be “drawing” in thefollowing case.

(2-1) Color number C<Cth and pixel density D≧Dth

Similarly, the attribute of the target region is determined to be“photo” in the following two cases.

(3-1) Distribution width W≧Wth and color number C≧Cth(3-2) Distribution width W<Wth and color number C≧Cth and pixel densityD≧Dth

Since the attribute of a target region is determined to be “text” forcase (1-1) in the embodiment, the attribute of an image region havingtypical text characteristics, i.e., a relatively low color number C anda relatively low pixel density D, can be accurately identified. Further,since the attribute of a target region is determined to be “drawing” forcase (2-1) in the embodiment, the attribute of an image region havingtypical drawing characteristics, i.e., a relatively low color number Cand a relatively high pixel density D, can be accurately identified.Further, since the attribute of a target region is determined to be“photo” for case (3-1) in the embodiment, the attribute of an imageregion having typical photo characteristics, i.e., a relatively largedistribution width W and a relatively high color number C, can beaccurately identified.

When a target image has a relatively small distribution width W, arelatively large color number C, and a relatively small pixel density D,the characteristics of the image match those of text, owing to therelatively small distribution width W and the relatively low pixeldensity D. The characteristics of the image conform to those of a photoonly with respect to the relatively large color number C, and the imagedoes not appear to have characteristics matching those of a drawing.Since the attribute of a target region is determined to be text in thecase of (1-2) in the embodiment, the attribute of the target region canbe accurately identified based on the high number of matchingcharacteristics, even though the characteristics are atypical for text.

For the scanned image SI in the example of FIG. 3, the regionidentification portion 120 determines the attributes of the three objectimages BK1-BK3 to be “text” and the attributes of the object images BK4and BK5 to be “photo” and “drawing,” respectively.

In S500 the scanner driver 100 determines whether the object image wasfound to have the attribute “text,” i.e., whether the object image is atext object image. If the object image is not a text object image (S500:NO), the scanner driver 100 advances to S600. However, if the objectimage is a text object image (S500: YES), in S550 the text binary imagegeneration portion 130 of the scanner driver 100 executes a text binarydata generation process in which text binary data is generated.

The text binary data generated in S550 is binary image data thatsuitably represents text and differs from the binary image datagenerated in S350 (see FIG. 5). Text binary data expresses text pixelsconfiguring text as “1” and non-text pixels that do not configure textas “0”. As will be described later, the text binary image generationportion 130 generates text binary data in this process for each objectimage found to have the attribute “text” (the three object imagesBK1-BK3 for the scanned image SI shown in FIG. 5). The scanner driver100 advances to S600 after completing the text binary data generationprocess.

In S600 the scanner driver 100 executes a compressed image datageneration process. FIG. 10 is a flowchart illustrating steps in thecompressed image data generation process, while FIGS. 11(A) and (B) arean explanatory diagram illustrating the process.

In S610 the scanner driver 100 generates background image data using thetext binary data and the scan data. That is, the scanner driver 100replaces pixel data (color values) in the scan data that corresponds totext pixels identified by the text binary data with the value of thebackground color, thereby generating background image data representinga background image BGI in which the text has been removed from thescanned image SI. The background color value represents the backgroundcolor in the scanned image SI around the text object. In the embodiment,the background color value is the average color value in the solidregion A1 surrounding the non-solid region A2, which includes the textobject. Hence, the region identification portion 120 uses the backgroundcolor average values BCR, BCG, and BCB, calculated for the solid regionA1 as thresholding reference values in S300 of FIG. 2, as the backgroundcolor values. FIG. 11(A) shows the background image BGI corresponding tothe scanned image SI in FIG. 3. The text object Ob2 (see FIG. 3) hasbeen omitted in the background image BGI, but the photo object Ob3 anddrawing object Ob4 remain.

In S620 the scanner driver 100 compresses the background image datagenerated in S610 to produce compressed background image data. Thebackground image BGI is a multilevel (256-level, for example) image thatmay include photos and drawings, but not text. The scanner driver 100generates the compressed background image data using JPEG (JointPhotographic Experts Group) compression, which is suitable forcompressing such multi-level images. Since the background image datadoes not include text, there are relatively few high-frequencycomponents. Consequently, the compression ratio for compressingbackground image data with JPEG compression is higher than that forcompressing the original image (scan data that includes text).

In S630 the scanner driver 100 compresses the text binary data toproduce compressed text binary data. FIG. 11(B) shows three text imagesTL1-TL3 represented by text binary data obtained by thresholding thethree object images BK1-BK3 in the scanned image SI. The text imagesTL1-TL3 are prone to poor legibility due to pronounced jagged edges whenresolution is reduced. The scanner driver 100 generates compressed textbinary data using a reversible compression method called ModifiedModified READ (MMR) that is capable of compressing the binary image dataat a high ratio without losing resolution.

In S640 the scanner driver 100 generates high-compression PDF data fromthe compressed background image data, the compressed text binary data,text color values expressing the color of text represented by thecompressed text binary data (RGB values expressing the text color), andcoordinate data. The text color values are identified in the text binarydata generation process described later (S550 of FIG. 2). The coordinatedata represents the relative positions of the text images TL1-TL3represented by the compressed text binary data in the background imageBGI represented by the compressed background image data usingcoordinates of the background image BGI. PDF standards have beenestablished for storing image data in a plurality of formats in a singlefile and for superimposing this image data in order to reproduce asingle image. The scanner driver 100 generates high-compression PDF datarepresenting the scanned image SI according to the PDF standards bystoring the compressed background image data and compressed text binarydata in a single file and by storing the text color values andcoordinate data in this file in association with the compressed textbinary data. In this way, the scanner driver 100 can save scanned imagesSI that contain text in a format that requires a relatively small amountof data yet produces sharp text. After generating high-compression PDFdata, the scanner driver 100 ends the current image process.

A-3: Text Binary Data Generation Process

FIG. 12 is a flowchart illustrating steps in the text binary datageneration process in S550 of FIG. 2. As described above, the textbinary image generation portion 130 executes the text binary datageneration process to generate text binary data for each text objectimage. The text binary data is binary image data that suitably renderstext.

In S551 of the text binary data generation process, the text binaryimage generation portion 130 selects one text object image to beprocessed. FIG. 13 shows the text object image BK2 as an example. Thefollowing description will assume that the text binary image generationportion 130 has selected the text object image BK2 to be processed.

In S552 the temporary identification portion 131, the background coloridentification portion 132, and the text color identification portion133 of the text binary image generation portion 130 execute a text coloridentification process to identify the text color value representing thecolor of characters in the text object image BK2.

FIG. 14 is a flowchart illustrating steps in the text coloridentification process. In S5521 at the beginning of this process, thetemporary identification portion 131 temporarily identifies text pixelsin the text object image BK2 to calculate a valid pixel number VMN. Morespecifically, the temporary identification portion 131 temporarilyidentifies object pixels (pixels having the value “1”) in binary imagedata for the text object image BK2 that was generated in S350 of FIG. 2as text pixels. The temporary identification portion 131 counts thetotal number of text pixels temporarily identified and calculates thevalid pixel number VMN by multiplying this total by a prescribedcoefficient (ratio). In the embodiment, the coefficient is set to 0.2.

In S5522 the background color identification portion 132 identifiesbackground color values representing the background color of the textobject image BK2. In the embodiment, the background color identificationportion 132 identifies the background color values using color data(pixel data) for the solid region A1 in the scanned image SI surroundingthe non-solid region A2 that includes the text object image BK2, becausethe background in the text object image BK2 is continuous with the solidregion A1 and, hence, likely has a color similar to that of the solidregion A1. More specifically, the background color identificationportion 132 identifies the background color values as the backgroundcolor average values BCR, BCG, and BCB in the solid region A1. Thesebackground color values are identical to the color values calculated inS300 as the thresholding reference values.

In S5523 the text color identification portion 133 selects one of thecomponents of the color system in which the text object image BK2 isexpressed (one of the RGB components in the embodiment).

In S5524 the text color identification portion 133 generates colordistribution data (a histogram) from the image data corresponding to theselected component (partial image data acquired from the scan data).Pixel data for each pixel in image data representing the text objectimage BK2 includes three component values for the RGB colors. In otherwords, image data representing the text object image BK2 can includeimage data for three components. The component image data for eachcomponent is image data configured of one of the three component values.The color distribution data is generated from the component image datacorresponding to the selected component for the text pixels describedabove.

FIG. 15 shows an example of a histogram for the text object image BK2,and specifically for the R component of the text object image BK2 (the Rcomponent image data). A segment HGR1 of the plotted line depicted as asolid line in FIG. 15 is a histogram of the set of text pixels in thetext object image BK2 that were temporarily identified in S5522. Thesegment HGR2 depicted as a dashed line is a histogram of pixels otherthan the text pixel set (i.e., pixels representing the backgroundareas).

In S5525 the text color identification portion 133 uses the histogramgenerated in S5524 to identify a maximum component value Mx and aminimum component value Mn of the text pixel set temporarily identifiedin S5522. In S5526 the text color identification portion 133 compares adifference DW1 between the gradation value of the component selectedfrom the background color values (hereinafter “background colorcomponent value”) and the maximum component value Mx and a differenceDW2 between the background color component value and the minimumcomponent value Mn. The text color identification portion 133 determineswhich of the differences DW1 and DW2 is greater, i.e., which of themaximum component value Mx and minimum component value Mn differs mostfrom the background color component value. FIG. 15 shows an example inwhich the difference DW2 between the minimum component value Mn andbackground color component value (BCR) is greater than the differenceDW1 between the maximum component value Mx and the background colorcomponent value (BCR). Here, the background color average value BCR isused as the background color component value. This case may arise whenthe background color approaches white and the text color approachesblack, for example. There are other cases in which the difference DW1between the maximum component value Mx and the background colorcomponent value (BCR) may be greater than the difference DW2 between theminimum component value Mn and the background color component value BCR,such as when the background color approaches black and the text colorapproaches white.

If the minimum component value Mn differs most from the background colorcomponent value (S5526: Mn), in S5527 the text color identificationportion 133 calculates, as the text color component value, the averagecomponent value of text pixels having component values in the range fromthe minimum component value Mn to an uppermost valid component value VV.The uppermost valid component value VV is set so that the number of textpixels having component values in the range from the minimum componentvalue Mn to the uppermost valid component value VV is equivalent to thevalid pixel number VMN. Specifically, the text color identificationportion 133 sequentially selects one component value at a time from thehistogram, beginning in order from the minimum component value Mn, andcounts the number of text pixels having the selected component value.The text color identification portion 133 repeats this process ofsequentially selecting a component value and counting the number of textpixels with that component value until the total count of the selectedcomponent values exceeds the valid pixel number VMN. The text coloridentification portion 133 ends the process of selecting componentvalues and counting text pixels once this total count exceeds the validpixel number VMN, and computes the average component value for the validpixel number VMN of text pixels counted up to that point. The example inFIG. 15 shows a shaded area VA of the histogram that includes a numberof pixels equivalent to the valid pixel number VMN. In this example, thetext color identification portion 133 calculates an average componentvalue TCR for the R components of the pixels included in the shaded areaVA as the text color component value.

When the maximum component value Mx differs most from the backgroundcolor component value (S5526: Mx), in S5528 the text coloridentification portion 133 calculates the average component value oftext pixels having component values within a range from the maximumcomponent value Mx to a lowermost valid component value VU as the textcolor component value. The lowermost valid component value VU is set sothat the number of text pixels having component values within a rangefrom the maximum component value Mx to the lowermost valid componentvalue VU is equivalent to the valid pixel number VMN. Specifically, thetext color identification portion 133 calculates the text colorcomponent value by sequentially selecting one component value at a time,beginning from the maximum component value Mx, and executing a processsimilar to S5527 described above. The average component value TCR forthe R component is indicated in FIG. 15 as the text color componentvalue.

In S5529 the text color identification portion 133 determines whetherall three components in the color system have been selected. If allthree components have been selected (S5529: YES), the text coloridentification portion 133 ends the text color identification process.However, if there remain components that have not been selected (S5529:NO), the text color identification portion 133 returns to S5523, selectsan unprocessed component, and repeats the process in S5524-S5528described above.

When the text color identification process is completed, text colorcomponent values have been calculated for all components in the colorsystem. As a result, the text color identification portion 133 hasidentified a text color value comprising this set of text colorcomponent values. In the embodiment, the text color identificationportion 133 identifies a text color value comprising text colorcomponent values TCR, TCG and TCB expressed in the RGB color system.Here, the text object image BK2 is one part of the scanned image SIrepresented by scan data generated by the scanner 300 or theimage-reading unit of the multifunction peripheral 400. As a result ofthis process, as shown in FIG. 13, an intermediate color region TEAhaving an intermediate color between the background color and text colorcan be expressed near the edge of the text object image BK2 between thebackground and the text. In the text color identification processdescribed above, the text color identification portion 133 calculates atext color using a prescribed ratio of text pixels ranging sequentiallyfrom the color farthest from the background color (the color having acomponent value that differs most from that of the background color) tothe back ground color in the histogram. In relation to the text objectimage BK2, it can be said that the text color identification portion 133calculates the text color using text pixels in a region TCA positionedfarthest from edge between the text and the background (hereinafterreferred to as a text core area TCA.

In S553 of FIG. 12, the difference acquisition portion 134 calculatesthe differences between the background color value (BCR, BCG, BCB) andthe text color value for all components in the color system (TCR, TCG,TCB). As a result of this process, the difference acquisition portion134 acquires differentials DFR, DFG, and DFB for the three components R,G, and B. The differential DFR for the R component is the absolute valueof (BCR−TCR). Similarly, the differential DFG for the G component is theabsolute value of (BCG−TCG) and the differential DFB of the B componentis the absolute value of (BCB−TCB).

In S554 the selection portion 135 selects a specific componentcorresponding to the maximum differential among the three differentialsDFR, DFG, and DFB to serve as a representative component. FIGS.16(A)-(C) illustrate the method of selecting this representativecomponent. FIG. 16(A)-(C) show histograms HGR, HGG, and HGB thatrespectively indicate the R component image data, G component imagedata, and B component image data included in a single text object image.In this example, the differentials of the three components in the RGBcolor system all differ from one another. Specifically, the relationshipof the three component differentials is (differential DFR of Rcomponent)>(differential DFB of B component)>(differential DFG of Gcomponent). Accordingly, the selection portion 135 selects the Rcomponent as the representative component in this example. That is, theselection portion 135 selects the R component image data as thecomponent image data to be used in steps S555 and S556 described later.

In S555 of FIG. 12, the characteristic value setting portion 136 and thethreshold value setting portion 137 execute a threshold value settingprocess. FIG. 17 is a flowchart illustrating steps in the thresholdvalue setting process. In S5551 the characteristic value setting portion136 acquires the average value of the representative component in thetext object image currently being processed. The average value of therepresentative component is found by averaging the representativecomponent for all text pixels included in the text object image. Forexample, an average value AVR of the R component shown in FIG. 15 is theaverage value of the R component for the set of pixels constituting thetext object image that have an R component value in the range from theminimum component value Mn to the maximum component value Mx.

In S5552 the characteristic value setting portion 136 acquires acharacteristic value TL related to the sharpness of the text(hereinafter referred to as “text sharpness TL”) using the gradationvalue of the representative component identified above among the textcolor value (hereinafter referred to as the “text color component valueof the representative component”) and the average value of therepresentative component. That is, the characteristic value settingportion 136 computes the absolute value SL of (text color componentvalue of the representative component)−(average component value of therepresentative component). The text sharpness TL is the inverse of theabsolute value SL (TL=1/SL). Here, text has less blurriness when thetext sharpness TL is higher and more blurriness when the text sharpnessTL is lower. Hence, the absolute value SL may be called an index valuerepresenting the indistinctness or blurriness of text. In other words, arelatively high text sharpness TL (relatively large value) denotes arelatively low degree of blurriness, while a relatively low textsharpness TL (relatively small value) denotes a relatively high degreeof blurriness. FIG. 15 shows the absolute value SL of the R component(average component value AVR−text color component value TCR), which isthe representative component.

In S5553 the threshold value setting portion 137 uses the text sharpnessTL to set a threshold value TH used to generate text binary data in thethresholding process. More specifically, the threshold value settingportion 137 first calculates a parameter PR using the followingequation.

PR=2×(1/TL)−20  Equation (5)

Next, the threshold value setting portion 137 calculates the thresholdvalue TH according to the following method using the parameter PRcalculated above. That is, the threshold value setting portion 137 setsthe threshold value TH to a value obtained by shifting the text colorcomponent value of the representative component by the parameter PRtoward the background color component value of the representativecomponent. In other words, if (text color component value of therepresentative component)≦(background color component value of therepresentative component), e.g., when the background is brighter thanthe text, the threshold value setting portion 137 sets the thresholdvalue TH to (text color component value of the representativecomponent)+(parameter PR). When the (text color component value of therepresentative component)>(background color component value of therepresentative component), e.g., when the text is brighter than thebackground, the threshold value setting portion 137 sets the thresholdvalue TH to (text color component value of the representativecomponent)−(parameter PR). In this way, the threshold value settingportion 137 can set a suitable threshold value TH between the text colorcomponent value and the background color component value of therepresentative component. FIG. 15 shows the threshold value THR for theR component, which is the representative component in this example.Since the text color component value TCR background color value BCR inthe example of FIG. 15, the threshold value THR is set to (text colorcomponent value TCR+parameter PR).

In S556 of FIG. 12 the thresholding process portion 138 executes athresholding process on the component image data of the representativecomponent using the threshold value TH. Through this process, thethresholding process portion 138 generates text binary data representingtext in the text object image BK2. An example in which therepresentative component is the R component will be described here. WhenR component value≦background color value BCR, the thresholding processportion 138 generates the text binary data by setting all R componentvalues in the R component image data that are less than or equal to thethreshold value THR to “1” and all R component values greater than thethreshold value THR to “0”. However, if background color value BCR<Rcomponent value, the thresholding process portion 138 generates the textbinary data by setting all R component values in the R component imagedata that are greater than or equal to the threshold value THR to “1”and all R component values less than the threshold value THR to “0”.

In S557 the text binary image generation portion 130 determines whetherall text object images have been subjected to the above process. If alltext object images have been processed (S557: YES), the text binaryimage generation portion 130 ends the text binary data generationprocess. However, if there remain unprocessed text object images (S557:NO), the text binary image generation portion 130 returns to S551,selects an unprocessed text object image, and repeats the process insteps S552-S556 described above.

According to the text binary data generation process of the embodimentdescribed above, the text binary image generation portion 130 sets thethreshold value TH using the background color value of the text objectimage (the background color component value BCR, for example), the textcolor value (the text color component value TCR, for example), and thetext sharpness TL. In this way, the text binary image generation portion130 can generate text binary data that renders text appropriately basedon the sharpness of the text.

As in the example of the text object image BK2 described with referenceto FIG. 13, a text core area TCA and an intermediate color region TEApositioned on the periphery of the text core area TCA (in a region nearthe edge between the text and background) appears in text of the scannedimage. Thus, it is possible to determine that the text sharpness TL oftext is lower when the intermediate color region TEA of the text imageis larger (wider), indicating that the text is more indistinct (theoutline of the characters is blurry). Similarly, it is possible todetermine that the text sharpness TL is higher when the intermediatecolor region TEA of the text image is smaller (thinner), indicating thatthe text is less indistinct (the outline of the characters is sharp). Ifa suitable threshold value TH cannot be set for text binary data in suchtext object images, textual elements represented by the text binaryimage (primarily fine lines) might be formed at an inappropriate width.

FIGS. 18(A)-18(C) are explanatory diagrams illustrating the effects ofthe embodiment. FIG. 18(A) shows a sample text image rendered by thetext binary data generated in the text binary data generation processaccording to the embodiment shown in FIG. 12. FIG. 18(B) is a firstcomparative example of a text image rendered by binary data generatedwhen the threshold value TH is set too close to the background colorvalue (too far from the text color value). Elements of the characters inthe text image of the first comparative example are excessively thick,making the characters less attractive and more indistinct than the textimage of the embodiment (FIG. 18(A)). For example, elements in the thirdcharacter from the left (@) run together when they should be apart.

FIG. 18(C) is a second comparative example of a text image rendered bybinary data generated when the threshold value TH is set too far fromthe background color value (too close to the text color value). Elementsof the characters in the text image of the second comparative exampleare excessively thin, making the characters less attractive and moreindistinct than the text image of the embodiment (FIG. 18(A)). Forexample, the second character from the right (¥) and the third characterfrom the right (#) have breaks in some of the thin lines.

In contrast, the scanner driver 100 according to the embodiment can seta suitable threshold value TH for thresholding using the text sharpnessTL. Thus, the scanner driver 100 can avoid a drop in the appearance ordistinctness of text represented by text binary data, as shown in FIG.18(A).

FIGS. 19(A) and 19( b) illustrate the relationship between the textsharpness TL of text and the threshold value TH set in the embodiment.FIG. 19(A) shows a histogram HG1 of component values for a prescribedcomponent when the text sharpness TL is relatively low. FIG. 19(B) showsa histogram HG2 of component values for a prescribed component when thetext sharpness TL is relatively high. As can be seen from the graphs,the breadth of the histogram HG1 in FIG. 19(A) (indicated in dispersion,for example) is relatively large when the text sharpness TL isrelatively low. In other words, the difference between the text colorvalue (the color of the text core area TCA in FIG. 13) and the averagecolor value for the entire text is great, and the absolute value SL islarge. On the other hand, the breadth of the histogram HG2 is relativelysmall when the text sharpness TL is relatively high. In other words, thedifference between the text color value and the average color value issmall, and the absolute value SL is small. In this way, thecharacteristic value setting portion 136 can set a suitable textsharpness TL based on the difference between the text color value andthe average color value of the text pixel set temporarily identifiedfrom the binary image data generated in S350 of FIG. 2.

As described above, the threshold value setting portion 137 of theembodiment calculates the threshold value TH by shifting the text colorcomponent value of the representative component a parameter PR in thedirection toward the background color component value of therepresentative component. The parameter PR is calculated to correspondto the amount that the threshold value TH deviates from the text colorcomponent value using the equation PR=2×(1/TL)−20. Hence, when the textsharpness TL is relatively high (when the blurriness of the text isrelatively low), the threshold value setting portion 137 sets thethreshold value TH such that the difference between the text color valueand the threshold value TH is relatively small. When the text sharpnessTL is relatively low (when the blurriness of the text is relativelyhigh), the threshold value setting portion 137 sets the threshold valueTH such that the difference between the text color value and thethreshold value TH is relatively large. Therefore, the text binary datageneration process of the embodiment can set a suitable threshold valueTH for both cases in which the text sharpness TL is relatively low (inwhich the text blurriness is relatively high) and cases in which thetext sharpness TL is relatively high (in which the text blurriness isrelatively low), thereby producing text binary data that renders textappropriately.

The text binary data generation process of the embodiment also generatesbinary image data using component image data for one specific componentin the color system used by the scan data, e.g., one of the RGBcomponents in the RGB color space. This specific component has themaximum component differential between its text color value andbackground color value among the three component differentials DFR, DFG,and DFB. If the component differential is small, there is a possibilitythat a slight difference in the position of the threshold value TH couldcause a major change in the thresholding results. Consequently, it issometimes difficult to set a suitable threshold value TH for componentimage data having a small component differential.

Suppose, for example, that the threshold value TH were set slightlycloser to the text color value than the optimum threshold value TH forcomponent image data having a relatively small component differential. Atext image rendered by text binary data that was generated using thiscomponent image data and this threshold value TH would very likely haveelements that are excessively thin, as in the second comparative exampleshown in FIG. 18(C). Conversely, suppose that the threshold value THwere set slightly closer to the background color value than the optimumthreshold value TH for component image data having a relatively smallcomponent differential. A text image rendered by text binary data thatwas generated using this component image data and this threshold valueTH would very likely have elements that are excessively thick, as in thefirst comparative example shown in FIG. 18(B). When the component imagedata has a relatively high component differential, on the other hand,the text image rendered by text binary data that was generated usingthis component image data, even when using a threshold value TH that isoffset from the optimum threshold value TH, is unlikely to differgreatly from a text image produced when using the optimum thresholdvalue TH. Hence, the range of allowable threshold values TH is increasedwhen using component image having a relatively large componentdifferential, making it possible to produce stable text binary data thatsatisfactorily renders the text image. The scanner driver 100 accordingto the embodiment can generate text binary data that suitably renderstext using component image data having the largest componentdifferential.

Further, consider an extreme example when the background color is white,i.e, when the RGB values for 256 levels are (255, 255, 255), and thetext color is yellow, i.e, when the RGB values are (255, 255, 0). Thisexample illustrates how there can be almost no component differential inthe first component image data (the R component image data or Gcomponent image data, for example) while there is a large componentdifferential in the second component image data (the B component imagedata, for example) for some combinations of background color values andtext color values. In such cases, it is very difficult to produce textbinary data using the first component image data. However, suitable textbinary image data can easily be produced using the second componentimage data, which has a large component differential. Hence, the scannerdriver 100 according to the embodiment can easily produce suitable textbinary image data regardless of the combination of background colorvalues and text color values.

Further, when the background color is white and the text color isyellow, the difference between the brightness value Y of the backgroundcolor and the brightness value Y of the text color is not great. As isclear from the formula for calculating the brightness value Y,Y=((0.298912×R)+(0.586611×G)+(0.114478×B)), the contribution of the Bcomponent to the difference in the brightness value Y between the yellowand white colors is much smaller than the contribution of the R and Gcomponents. In other words, there is a possibility that suitable textbinary data cannot be generated for some combinations of backgroundcolor values and text color values, even when using brightness imagedata. However, the scanner driver 100 according to the embodiment caneasily produce suitable text binary image data using the component imagedata having the largest component differential.

In the embodiment, the thresholding value setting portion 137 determinesthe threshold value TH by using the specific component of the threecolor components and the text color value of the specific component.Here, the specific component is a component having the maximumdifferential. Specifically, the thresholding value setting portion 137compares the background color value of the specific component and thetext color value of the specific component, and calculate the thresholdvalue TH by shifting the text color value of the specific component bythe parameter PR toward the background color value of the specificcomponent. If the differential between the background color value of thespecific component and the text color value of the specific component(the component of the difference corresponding to the specificcomponent) is small, it will be sometimes difficult to calculateproperly the threshold value TH unless the values (the background colorvalue of the specific component, the text color value of the specificcomponent, and the parameter PR) are properly determined with highaccuracy. However, in the embodiment, the threshold value TH can beeasily calculated by using the background color value of the specificcomponent corresponding to the maximum differential and the text colorvalue of the specific component.

The text color identification portion 133 according to the embodimentidentifies the text color value using the background color value.Further, the text color identification portion 133 identifies the textcolor value using some of the pixels from the temporarily identifiedtext pixel set. Thus, the text color identification portion 133 cansuitably identify a text color value. Specifically, the text coloridentification portion 133 of the embodiment identifies the maximumcomponent value Mx and the minimum component value Mn in the temporarilyidentified pixel set from a histogram of the object image. The textcolor identification portion 133 identifies the text color value using apixel set (including pixels of the valid pixel number VMN) havingcomponent values close to the maximum component value Mx or the minimumcomponent value Mn that differs most from the background color value.Hence, the pixel set used for identifying the text color value includesthe color value (one of the maximum component value Mx and the minimumcomponent value Mn) among the temporarily identified text pixel sethaving the greatest difference from the background color value.Therefore, the text color identification portion 133 can identify thetext color value to be a value representing the color of the text corearea TCA in the characters (see FIG. 13), which is less likely to bedependent on the text sharpness or background color value. As a result,the text color identification portion 133 can identify a suitable textcolor value, regardless of the text sharpness (blurriness) or the typeof background color.

In the embodiment, the background color identification portion 132identifies the background color value using pixel values in the solidregion A1 of the scanned image SI that surrounds the text object image.Since the background color identification portion 132 identifies thebackground color value using a portion of the scan data representing thescanned image SI that differs from the portion of data representing thetext object image, the background color identification portion 132 canidentify the background color value appropriately based on a portion ofthe scanned image SI different from the text object image.

B. Variations of the Embodiments

(1) In the embodiment, the parameter PR is calculated by using the indexvalue SL, and the threshold value TH is calculated by using theparameter PR. However, the threshold value can be calculated otherwise.For example the threshold value TH can be calculated by using followingEquation (6):

TH=text color component value×PR2+background color component valueX×(1−PR2)  Equation (6)

Here, PR2 is a designed value set in a following range: 0<PR2<1. Forexample, if the text rendered by the text binary data is required to bethin, the designed value PR2 is set to be a relatively small value. Onthe other hand, if the text rendered by the text binary data is requiredto be thick, the designed value PR2 is set to be a relatively largevalue.

(2) In the embodiment, the text sharpness TL (equivalent to 1/SL, whereSL is the absolute value of (average component value AVR−text colorcomponent value TCR)) is used as the characteristic value for textsharpness, but various other characteristic values may be used. Forexample, statistics correlated with the breadth of peaks in thehistogram of a text object image may be used as the characteristicvalue, such as the inverse of dispersion or the height of the peakrelative to the number of text pixels. Other possible characteristicvalues are characteristics of the image-reading unit (the scanner 300 orthe like) used to generate the scan data, such as characteristics of theoptical sensor (CCDs or the like), light sources, and other components,and particularly device characteristics related to text sharpness.

Further, the background color value of the text object image was set tothe average color value of all pixels in a solid region surrounding thetext object image, but the present invention is not limited to thisvalue. For example, the background color value of the text object imagemay be set to the average color value in a ring-like region surroundingthe text object image that has a prescribed width. The prescribed widthmay be equivalent to between one and a few blocks B set when identifyingthe regions (see FIG. 4). The background color identification portion132 may also identify the background color value using pixels in thetext object image. For example, the background color identificationportion 132 may identify the background color value using all or some ofthe pixels in the text object image, excluding the text pixels.

The present invention may also apply to other methods of identifyingtext color, in addition to the method described in the embodiments. Forexample, the text color identification portion 133 may set the range ofcomponent values in the histogram of the text object image (for example,a range between maximum component value Mx and the minimum componentvalue Mn) such that the number of the pixels in the range is greaterthan or equal to a reference value, and set the text color componentvalue to a value in the range. This value is positioned at a prescribedposition from one end of the range opposite to a side of the backgroundcolor component value (a position in the first quarter of the range ofcomponent values, for example).

(3) In the embodiment described above, the difference acquisitionportion 134 calculates component differentials (the difference betweenthe text color value and the background color value for each component)using a histogram of component image data for each component in the RGBcolor space. Next, the difference acquisition portion 134 selects thecomponent having the largest component differential as a representativecomponent. However, the difference acquisition portion 134 may calculatedifferentials for components in other color systems, such as the threecomponents in the YCaCb color system or the three components in theCIELAB color system. In this case, the selection portion 135 may selectthe component having the largest differential among the components inthe color system to be used as the representative component. Thus, thecomponents constituting the color system may all be components relatedto color, as in the components of the RGB color system, or may includecomponents not related to color, as with the brightness component (Y) inthe YCaCb color system.

(4) In the embodiment, the region identification portion 120 performs athresholding process based on background color values in order totemporarily identify text pixels (S600 of FIG. 2), prior to generatingthe final text binary data, but other methods may be used to temporarilyidentify text pixels. For example, the temporary identification portion131 may identify a partial set of the pixels constituting the textobject image, excluding pixels having color values within a prescribedrange that includes the background color value, as the set of pixelsconstituting text. Through this process, the provisional set of pixelsconstituting text can easily be identified.

(5) The image-processing functions provided in the scanner driver 100 ofthe computer 200 according to the embodiment may instead be provided ina device having an image-reading unit, such as the multifunctionperipheral 400 or the scanner 300, or a device having an optical imagedata generating unit, such as a digital camera. In such cases, amultifunction peripheral or a scanner possessing the image-processingfunctions may perform image processes on scan data using its ownimage-reading unit to generate processed image data (such ashigh-compression PDF data), and may output the processed image data to apersonal computer or the like that is connected to and capable ofcommunicating with the multifunction peripheral or the scanner.

In general, the device implementing the image-processing functions ofthe scanner driver 100 is not limited to the computer 200, but may be amultifunction peripheral, a digital camera, a scanner, or the like.Further, the image-processing functions of the embodiment may beimplemented on a single device or may be divided among a plurality ofdevices connected to each other over a network. In this case, the entiresystem of devices that implement the image-processing functionscorresponds to the image processing device of the present invention.

(6) Part of the processes implemented in hardware in the embodiment maybe replaced with software processes, while conversely part of theprocesses implemented in software may be replaced with a hardwareconfiguration.

While the invention has been described in detail with reference to theembodiment thereof, it would be apparent to those skilled in the artthat various changes and modifications may be made therein withoutdeparting from the scope of the invention.

What is claimed is:
 1. An image processing device comprising: aprocessor configured to perform: acquiring target image datarepresenting a target image including a letter, the target image dataincluding a plurality of component image data corresponding respectivelyto a plurality of components constituting a color coordinate system; andgenerating binary image data representing the letter in the target imageby using the target image data, the generating of the binary image datacomprising: identifying a background color value representing color ofbackground of the target image, the background color value beingcomposed of values for the plurality of components; identifying a lettercolor value representing color of the letter in the target image, theletter color value being composed of values for the plurality ofcomponents; acquiring a difference between the background color valueand the letter color value, the difference including a plurality ofcomponent differences corresponding respectively to the plurality ofcomponents; selecting, from among the plurality of component image data,one specific component image data corresponding to a specific componentfrom among the plurality of components, the specific componentcorresponding to a maximum component difference among the plurality ofcomponent differences; and performing a binarizing process on theselected one specific component image data to generate one binary imagedata.
 2. The image processing device according to claim 1, wherein thegenerating of the binary image data further comprises: determining abinarizing threshold value by using a component value that composes thebackground color value and corresponds to the specific component, and acomponent value that composes the letter color value and corresponds tothe specific component, wherein the performing of the binarizing processon the selected one specific component image data is performed by usingthe determined binarizing threshold value corresponding to the specificcomponent.
 3. The image processing device according to claim 1, whereinthe generating of the binary image data further comprises: identifying apixel group that includes a plurality of pixels configuring the targetimage excluding pixels having color values in a prescribed rangeincluding the background color value, wherein the identifying of theletter color value is performed by using a part of pixels of the pixelgroup.
 4. The image processing device according to claim 1, wherein thetarget image data represents the target image that is a part of parentimage represented by parent image data, wherein the identifying of thebackground color value is performed by using partial data of the parentimage data, the partial data being different from the target image data.5. The image processing device according to claim 1, wherein theidentifying of the letter color value is performed by using thebackground color value.
 6. A computer-readable storage medium storingcomputer-readable instructions that, when executed by a processor,causes an image processing device to perform: acquiring target imagedata representing a target image including a letter, the target imagedata including a plurality of component image data correspondingrespectively to a plurality of components constituting a colorcoordinate system; and generating binary image data representing theletter in the target image by using the target image data, thegenerating of the binary image data comprising: identifying a backgroundcolor value representing color of background of the target image, thebackground color value being composed of values for the plurality ofcomponents; identifying a letter color value representing color of theletter in the target image, the letter color value being composed ofvalues for the plurality of components; acquiring a difference betweenthe background color value and the letter color value, the differenceincluding a plurality of component differences correspondingrespectively to the plurality of components; selecting, from among theplurality of component image data, one specific component image datacorresponding to a specific component from among the plurality ofcomponents, the specific component corresponding to a maximum componentdifference among the plurality of component differences; and performinga binarizing process on the selected one specific component image datato generate one binary image data.
 7. The computer-readable storagemedium according to claim 6, wherein the generating of the binary imagedata further comprises: determining a binarizing threshold value byusing a component value that composes the background color value andcorresponds to the specific component, and a component value thatcomposes the letter color value and corresponds to the specificcomponent, wherein the performing of the binarizing process on theselected one specific component image data is performed by using thedetermined binarizing threshold value corresponding to the specificcomponent.
 8. The computer-readable storage medium according to claim 6,wherein the generating of the binary image data further comprises:identifying a pixel group that includes a plurality of pixelsconfiguring the target image excluding pixels having color values in aprescribed range including the background color value, wherein theidentifying of the letter color value is performed by using a part ofpixels of the pixel group.
 9. The computer-readable storage mediumaccording to claim 6, wherein the target image data represents thetarget image that is a part of parent image represented by parent imagedata, wherein the identifying of the background color value is performedby using partial data of the parent image data, the partial data beingdifferent from the target image data.
 10. The computer-readable storagemedium according to claim 6, wherein the identifying of the letter colorvalue is performed by using the background color value.